Verify with Energy Atlas: monthly consumption at neighborhood level in 2016.

Reading data

Neighborhood geometry shapefile is from an email from Eric Daniel Fournier.

## Reading layer `neighborhoods' from data source 
##   `/Users/yujiex/Dropbox/workLBNL/EESA/code/im3-wrf/energyAtlas/Neighborhood/neighborhoods/neighborhoods.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 476 features and 10 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -306628 ymin: -604427.3 xmax: 289125.5 ymax: 97257.92
## CRS:           3310

The following is a preview of the neighborhood data.

## Simple feature collection with 6 features and 10 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -201772.9 ymin: -565384.3 xmax: 275665.5 ymax: -43132.43
## CRS:           3310
##   neighborho pop2014 med_income owned_unit rent_units total_unit  pop_sqmi
## 1         39      NA         NA         NA         NA         NA        NA
## 2        251    7194     144250       2308        294       2602 1886.2738
## 3        195    3868     107475       1192         95       1287  252.3039
## 4        106   50335      90000      11841       5907      17748 3301.7585
## 5        452   33529     142998       7455       5337      12792 1688.3076
## 6         48      NA         NA         NA         NA         NA        NA
##                    name  pct_own  pct_rent                       geometry
## 1 scripps miramar ranch       NA        NA MULTIPOLYGON (((275072.1 -5...
## 2 rolling hills estates 88.70100 11.299001 MULTIPOLYGON (((152638.6 -4...
## 3          leona valley 92.61849  7.381507 MULTIPOLYGON (((151115.6 -3...
## 4            chatsworth 66.71738 33.282623 MULTIPOLYGON (((130208.8 -4...
## 5           foster city 58.27861 41.721388 MULTIPOLYGON (((-199586.1 -...
## 6      ncfua subarea ii       NA        NA MULTIPOLYGON (((259959.2 -5...

The following is a summary of the neighborhood shapefile data. The “neighborho” column is used in matching the neighborhood geometry with Energy Atlas neighborhood level energy.

##    neighborho     pop2014         med_income       owned_unit    
##  1      :  1   Min.   :     0   Min.   : 15532   Min.   :     0  
##  2      :  1   1st Qu.: 11308   1st Qu.: 58097   1st Qu.:  1949  
##  3      :  1   Median : 25588   Median : 82978   Median :  3985  
##  4      :  1   Mean   : 42468   Mean   : 90125   Mean   :  7252  
##  5      :  1   3rd Qu.: 53953   3rd Qu.:110782   3rd Qu.:  8772  
##  6      :  1   Max.   :992078   Max.   :250001   Max.   :176533  
##  (Other):470   NA's   :55       NA's   :62       NA's   :55      
##    rent_units       total_unit        pop_sqmi                        name    
##  Min.   :     0   Min.   :     0   Min.   :      0   military facilities:  3  
##  1st Qu.:  1237   1st Qu.:  3919   1st Qu.:   2460   san jose           :  3  
##  Median :  4201   Median :  9136   Median :   6372   brentwood          :  2  
##  Mean   :  7106   Mean   : 14357   Mean   :  16512   chinatown          :  2  
##  3rd Qu.:  8636   3rd Qu.: 17668   3rd Qu.:  12251   downtown           :  2  
##  Max.   :132663   Max.   :309196   Max.   :3006699   fairfax            :  2  
##  NA's   :55       NA's   :55       NA's   :55        (Other)            :462  
##     pct_own          pct_rent     
##  Min.   :  0.00   Min.   :  0.00  
##  1st Qu.: 40.48   1st Qu.: 30.05  
##  Median : 55.60   Median : 44.40  
##  Mean   : 54.29   Mean   : 45.71  
##  3rd Qu.: 69.95   3rd Qu.: 59.52  
##  Max.   :100.00   Max.   :100.00  
##  NA's   :58       NA's   :58

Following is the neighborhood geometry restricted within the boundary of LA county. This is the area we’ll analyze.

## Reading layer `la-county-boundary' from data source 
##   `/Users/yujiex/Dropbox/workLBNL/EESA/code/im3-wrf/domain/la-county-boundary.geojson' 
##   using driver `GeoJSON'
## Simple feature collection with 7 features and 17 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -118.9446 ymin: 32.79521 xmax: -117.6464 ymax: 34.8233
## CRS:           4326

Join building to neighborhood by first computing the building centroids, and check which neighborhood polygon contains the centroid.

The following is a preview of the matching result data frame

OBJECTID neighborho
19 78
20 78
21 78
23 78
24 78
25 78

The following is a summary of the matching

Summary stats of the number of buildings matched to a neighborhood
min Q1 median mean Q3 max
2 1514.75 3505.5 5354.216 6828.25 67309

Read neighborhood level annual energy data, usage_bld_kwh.csv, downloaded from the Energy Atlas website, https://ucla.app.box.com/s/z2i515cc2lgn3t6rpwe1ymcqygr4y5a0. Different from the Dropbox data, the new data has “masked” and NA’s in the usage column instead of the -7777, -8888, -9999 code. The “all” category is also removed in the new data set. “id” column is renamed to “geo_id”. The following is a preview of the usage_bld_kwh.csv

Preview of usage_bld_kwh.csv
geo_id sqft usage usage_med usage_med_sqft usetype year name solar_potential pop usage_percap
cities_1 1253 masked masked masked agriculture 2016 agoura hills NA NA masked
cities_1 4802115 65825608.0000 121901 9.7676 commercial 2016 agoura hills NA NA NA
cities_1 8906397 43707916.6000 9136 3.9839 condo 2016 agoura hills NA NA NA
cities_1 734698 masked masked masked industrial 2016 agoura hills NA NA masked
cities_1 100192 masked masked masked institutional 2016 agoura hills NA NA masked
cities_1 742420 2010646.5000 70823 3.2102 multi_family 2016 agoura hills NA NA NA

Join Energy Atlas energy data with neighborhood geometry * First split the “geo_id” column by the “_” * Filtering out the id’s at neighborhood level (prefix of “geo_id” == “neighborhoods”) * Join the energy and shapefile data with the numeric suffix of “geo_id” and the “neighborho” column in the shapefile

Filter the data by four steps. The following table shows the number of neighborhoods and records left after each step. The last step is meant to calculate the total usage for a neighborhood. As is shown here, the building types in EnergyAtlas overlaps. In order to compute the total of a neighborhood, we need to keep only the non-overlapping usetyeps. The definition of each use type are as following according to https://energyatlas.ucla.edu/methods

Usetypes in the EnergyAtlas data after filtering by size > 0
usetype definition
condo Condominiums
multi_family Duplexes to large apartment complexes.
res_total Sum of all residential categories.
single_family NA
residential_other Mobile home parks, manufactured homes, nursing homes, rural residential, and unknown other residential use codes that do not clearly fit within single family, multi-family, or condominium categories.
commercial Office buildings, hotels, retail, restaurants, mixed-use commercial, etc.
industrial Manufacturing, warehouses, processing facilities, extraction sites, etc.
institutional Schools, public hospitals, government owned facilities, churches, tax-exempt properties, etc.
agriculture Farms, agricultural lands, orchards, etc.
other Spans diverse range of use types unable to fit within the pre-set categories, including miscellaneous bus terminals, airports, vacant land, reservoirs, truck terminals, right-of-ways, etc.
Filtering steps and the number of neighborhoods and records remaining
Filtering Steps Number of Neighborhoods Number of Records
Original data 272 3264
Restrict to within LA county 263 3156
Remove masked data 248 1358
Keep records with positive sqft 248 1244
Remove agriculture and “other” usetype 248 1220
Restrict to the major usetypes 248 523

Following is a preview of the aggregated Energy Atlas data by neighborhood and by neighborhood and usetype

id.num usage m2 data.source
64 26652610 449924.7 Energy Atlas 2016
65 29456039 554136.2 Energy Atlas 2016
66 138283531 1801069.3 Energy Atlas 2016
67 15448257 236423.9 Energy Atlas 2016
68 313064947 3629159.2 Energy Atlas 2016
69 11983734 259334.2 Energy Atlas 2016
id.num usetype usage m2 data.source
100 res_total 50189240 951539.4 Energy Atlas 2016
101 res_total 91580166 1562195.4 Energy Atlas 2016
102 commercial 19134101 107975.5 Energy Atlas 2016
102 industrial 54522983 605488.6 Energy Atlas 2016
102 res_total 31257106 689366.6 Energy Atlas 2016
103 commercial 163046619 1183233.4 Energy Atlas 2016

Simulation results are saved in a csv file: annual_sim_result_by_idf_epw.csv

First aggregate simulation results to annual total, and convert the consumption from J to kwh.

Then map building types in the simulation data set to the EnergyAtlas types. Note that nursing homes are matched to residential rather than institutional. The following table shows the mapping from EnergyPlus models to EnergyAtlas types

Simulation type to EnergyAtlas type mapping
EnergyAtlas simulation
commercial FullServiceRestaurant
LargeHotel
LargeOffice
MediumOffice
RetailStandalone
SmallHotel
SmallOffice
SuperMarket
industrial HeavyManufacturing
LightManufacturing
Warehouse
institutional Hospital
NursingHome_baseline
PrimarySchool
Religious
SecondarySchool
res_total MidriseApartment
MultiFamily
SingleFamily

Following is a preview of the simulation results aggregated to neighborhood level

neighborho energy.kwh building.area.m2 FootprintArea.m2
64 7981954 53632.38 83553.09
65 120638671 447717.48 355309.44
66 176018035 1233231.33 1345776.95
67 3140236 22189.72 35505.44
68 915393216 2800466.98 2733022.22
69 41709827 211119.03 253185.40
neighborho usetype energy.kwh building.area.m2 FootprintArea.m2
64 industrial 641908.2 359.7088 529.0762
64 institutional 208216.9 631.3484 660.7435
64 res_total 7131829.3 52641.3205 82363.2751
65 commercial 21772060.1 21097.8687 9586.2425
65 industrial 5028840.1 5132.3534 4845.6390
65 institutional 8738001.4 10683.0355 11564.4421

Visualize neighborhood level simulation data

## Reading layer `grid_with_building' from data source 
##   `/Users/yujiex/Dropbox/workLBNL/EESA/code/im3-wrf/grid_with_building.geojson' 
##   using driver `GeoJSON'
## Simple feature collection with 62 features and 3 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: -118.885 ymin: 33.24275 xmax: -117.5225 ymax: 34.707
## CRS:           4326

Compare Energy Atlas and Simulation results

For most neighborhoods, the total building area recorded in Energy Atlas is larger than the area recorded in the simulation data set (building characteristics source data is from “Assessor_Parcels_Data_-_2019.csv” joined to the building geometry from LARIAC6_LA_County.geojson)

The following compares the building total sqft of the four major usetypes.

For most neighborhoods, the total building area recorded in Energy Atlas for each of the four major usetypes is larger than the area recorded in the simulation data set.

The following plots the difference in the percentage of each four usetypes in a neighborhood. We can see that simulation data sets have higher percentage of residential and industrial types and lower ratio of commercial buildings in most neighborhoods, compared against Energy Atlas data.

Compare the kwh/m2 and kwh between the two data set

The following shows the total energy usage and usage per total area comparison.

Compare the kwh/m2 usage between EnergyAtlas and simulation by different use types, restricted to the neighborhodds with data in both data sources

Compare the kwh usage between EnergyAtlas and simulation by different use types, restricted to the neighborhodds with data in both data sources

Compare the Distribution